Applied Time Series
General
Prefix
STAT
Course Number
427
Course Level
Undergraduate
Department/Unit(s)
College/School
College of Science and Engineering
Description
A study of the most useful techniques of analysis and forecasting using time series data. Topics include an introduction to forecasting, time series regression, decomposition methods, smoothing, smoothing techniques, basic techniques of Box-Jenkins methodology; use of statistical software.
Prerequisites
Credits
Min
3
Max
3
Repeatable
No
Goals and Diversity
MN Goal Course
No
Cultural Diversity
No
Learning Outcomes
Outcome
Derive autocorrelation functions for stationary time series such as AR and MA processes.
Outcome
Select appropriate time series models in the ARIMA family for time series data in different situations.
Outcome
Diagnose the fitting of an ARIMA model to a time series and forecast future values of the time series.
Outcome
Interpret analysis results and deliver findings with a written report.
Outcome
Use R or other software to analyze time series data, including the plots of sample autocorrelation function, sample partial autocorrelation function, and extended autocorrelation function.
Dependencies
Programs
STAT427
is a
completion requirement
for: